Triple
T18944002
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Suburbans |
E463459
|
entity |
| Predicate | starring |
P1507
|
FINISHED |
| Object | Tony Guma |
—
|
NE NERFINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Tony Guma | Statement: [The Suburbans, starring, Tony Guma]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tony Guma Context triple: [The Suburbans, starring, Tony Guma]
-
A.
Tony Guma
chosen
Tony Guma is a screenwriter best known for co-writing the 1999 comedy film "The Suburbans."
-
B.
Johnny Lovo
Johnny Lovo is a crime boss character in the 1932 gangster film "Scarface," serving as an early mentor and rival to the ambitious protagonist Tony Camonte.
-
C.
Tony Tanti
Tony Tanti is a former Canadian professional ice hockey forward best known for his high-scoring NHL career in the 1980s, particularly with the Vancouver Canucks.
-
D.
Sammy Angott
Sammy Angott was an American professional boxer and former world lightweight champion known for his crafty, awkward style and victories over several top fighters of his era.
-
E.
Phil Gubala
Phil Gubala is a notable resident associated with the community of Affton, Missouri.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69d8dcfec90481909e926be9767e5779 |
completed | April 10, 2026, 11:20 a.m. |
| NER | Named-entity recognition | batch_69e5d53e6e0c81908a547e21c4819bac |
completed | April 20, 2026, 7:26 a.m. |
Created at: April 10, 2026, 11:59 a.m.